In [2]:
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
In [3]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [4]:
stocks = px.data.stocks()
stocks.head()
Out[4]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [5]:
# YOUR CODE HERE
stocks.plot(x='date',y='MSFT')
plt.title('Microsoft Stock 2018-2019')
plt.ylabel('stock value')
plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [6]:
stocks.plot(x='date', y=['GOOG','AAPL', 'AMZN','FB', 'NFLX', 'MSFT'])
plt.title('Stock 2018-2019')
plt.ylabel('stock value')
plt.show()

Seaborn¶

First, load the tips dataset

In [7]:
tips = sns.load_dataset('tips')
tips.head()
Out[7]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [8]:
# Question: which group shows the tendency to give more tip? Smokers or Non-smokers?
g = sns.FacetGrid(tips, col='smoker', hue='sex')
g.map(sns.scatterplot, 'total_bill', 'tip')
plt.savefig('smoker.png', dpi=300)
plt.show()

# Answer: Non-smokers

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [9]:
# YOUR CODE HERE -> markers belum diganti based on line
df = px.data.stocks() 
fig = px.line(df, x='date', y=['GOOG','AAPL', 'AMZN','FB', 'NFLX', 'MSFT'], markers=True, symbol='variable', title='Stocks 2018-2019')
fig.update_traces(marker_symbol = 6, selector = dict(type='tirangle-left'))


fig.show()

The tips dataset¶

In [10]:
# YOUR CODE HERE
df = px.data.tips() 
fig = px.scatter(df, x='total_bill', y='tip', title='Tip Given Trend Based on Total Bill Paid', color='sex', facet_col='smoker', facet_row='time')
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [11]:
#load data
df = px.data.gapminder()
df.head()
Out[11]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [12]:
# Extract data
df = px.data.gapminder()
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()

# Use plotly bar
fig = px.bar(df_2007_new,  x="pop", y=df_2007_new.index, orientation='h', color= df_2007_new.index, 
    text_auto='.2s', title='Population of the World in 2007 Based on Continents' 
    )
fig.update_yaxes(categoryorder='max ascending')

fig.show()
In [ ]: